(182l) Investigating LecA Binding Mechanisms with a Cellular Membrane Containing Multiple Types of Receptors Via Kinetic Monte Carlo Simulation
AIChE Annual Meeting
2018
2018 AIChE Annual Meeting
Computing and Systems Technology Division
Interactive Session: Applied Mathematics and Numerical Analysis
Monday, October 29, 2018 - 3:30pm to 5:00pm
Our recent study has experimentally demonstrated that mixing different glycolipid receptors on cell membranes could results in the alternation of LecA binding capacity, avidity, and kinetics, which influence the downstream biological processes. However, no theoretical model can interpret the complex interplay between LecA and multiple glycolipids. Here, we aim to develop a kinetic Monte Carlo model (kMC) to simulate the kinetics of LecA binding to a cell membrane containing two types of receptors. The kMC framework is selected as it can track the temporal evolution of the surface configuration, which is important in the overall binding kinetics on a cell membrane [4]. Specifically, we represent a cell membrane as square lattices, where LecA receptors reside and are allowed to migrate; on the other hand, LecA are allowed to attach and detach from the simulation lattices during the simulation. The overall binding kinetics are represented by a few discrete events in the proposed kMC: (a) the attachment of a LecA protein (b) the detachment of a membrane-bound LecA protein (c) surface reactions between membrane-bound LecA proteins with receptors (d) receptor migration on a membrane. During the simulation, reaction rates of these events, which depend on the surface configuration, are calculated to randomly select one event to be performed at a time until the pre-specified end time. For the computational efficiency, the receptor migration event is decoupled as the rate of the receptor migration is overwhelmingly large [5]. We apply this methodology to systematically analyze the overall binding process and identify the most important factors. The kMC simulation not only supports our experimental results, but also offers a fundamental mechanism to predict the complex glycobiology processes on cell membrane surface.
[1] Grishin, A.V.; Krivozubov, M. S.; Karyagina A.S.; Gintsburg, A.L. Acta Naturae, 2015, 7, 29-41.
[2] Chi, E.; Mehl, T.; Nunn, D.; Lory, S. Interaction of Pseudomonas aeruginosa with A549 pneumocyte cells. Infect. Immun. 1991, 59, 822-828.
[3] Blanchard, B; Nurisso, A; Hollville, E; Tétaud, C; Wiels, J; Pokorná, M; Wimmerová, M; Varrot, A; Imberty, A. Structural basis of the preferential binding for globo-series glycosphingolipids displayed by Pseudomonas aeruginosa lectin I. J. Mol. Bio., 2008, 383, 837-853.
[4] Mayawala, K.; Vlachos, D. G.; Edwards, J. S. Spatial modeling of dimerization reaction dynamics in the plasma membrane: Monte Carlo vs. continuum differential equations, Biophys. J., 2006, 121, 194-208.
[5] Crose, M.; Kwon, J. S.; Nayhouse, M.; Ni, D.Christofides, P.D. On Operation of PECVD of Thin Film Solar Cells. Chem. Eng. Sci., 2015, 136, 50-61.
[6] Krishnan, P.; Singla, A.; Lee, C.A.; Weatherston, J.D.; Worstell, N.C.; Wu, H.-J. Hetero-multivalent binding of cholera toxin subunit B with glycolipid mixtures. Colloids Surf. B Biointerfaces, 2017, 160, 281-288.